These teachings relate generally to a bacterial detection platform based on Surface enhanced Raman scattering (SERS).
Illness and death due to pathogen-contaminated food supplies are on the rise, fueling concerns over safety of the nation's food supplies and the public's health, and leading to huge economic losses. According to the Centers for Disease Control and Prevention (CDC), in 2011, 31 major pathogens acquired in the United States caused upwards of 9.4 million episodes of foodborne illness, 55,961 hospitalizations, and 1,351 deaths.1 In 2005, the Food and Drug Administration (FDA) estimated that the costs of foodborne illness ranged between $10-$83 billion annually.2 Food pathogens such as Salmonella enterica and Listeria monocytogenes are “zero tolerated” in certain types of food products (e.g. “ready-to-eat” meats and dairy products, ground beef). Current food pathogen detection platforms, such as plating, polymerase chain reaction (PCR), and enzyme-linked immunosorbent assay (ELISA) require a significant step for cell enrichment, which is time consuming. Many of the rapid detection methods can't differentiate between live and dead cells, which may give an inaccurate estimation of the cells in the thermo processed food. A rapid, sensitive and accurate detection platform to detect bacteria cells in food matrices is essential to evaluate food contamination before the food products enter the supply chain.
Raman spectroscopy studies the molecular vibrations by light scattering, in which incident laser light is inelastically scattered from a sample and the wavelength of this light shifted in a manner of characteristic molecular vibrations. Placement of the sample of interest on noble metal nanoscale-roughened surfaces (typically silver or gold) tremendously enhances the inherently weak Raman molecular signatures, because of the large electromagnetic field induced by the excitation of the localized surface plasmon resonance (LSPR). This technique is so called surface enhanced Raman spectroscopy (SERS). Various SERS substrates have been fabricated and tested for bacteria. SERS detection capability is sensitive to the single bacterial cell/spore level. The capacity of SERS to discriminate pure bacterial sample is usually to species or even strain level with chemomatrics, such as principle component analysis (PCA) or hierarchical cluster analysis. The capabilities of using Ag dendrites in substrates for SERS have been greatly limited to detect bacterial cells in simple matrices.
There is a need for methods to separate bacteria from the matrices and concentrate the bacterial cells onto the SERS substrate. In addition, there is a need to discriminate between different species in a bacterial mixture.
There is a need for methods Ag dendrites in substrates for SERS for detecting bacteria in bacterial mixtures or in complex matrices.
Raman mapping technique is an advanced data collection technique for generating detailed chemical images based on a sample's Raman spectrum. A Raman spectrum is acquired at each and every pixel of the image, and then interrogated to generate artificial color images based on molecular composition and structure. A typical experiment uses sequential sample movement and spectrum acquisition to collect data from the user defined image area. This technique has been used widely in the characterization of eukaryotic cells and provides detailed information about the molecular composition of the subcellular volume being probed. The resolution of each pixel is usually about 0.5 to 3 μm, depending on the specific instrument
Many of the current SERS detection studies of bacterial cells in complex matrices are based on secondary labeling with a Raman reporter. However, the Raman reporter—modified nanoprobe provides only the signature of the reporters and tends to be an imaging tool rather than a detection probe. The intrinsic bacterial cell information is missing; therefore Raman reporter based SERS method can't be used for differentiation between dead and live cells. In addition, the use of a secondary label significantly increases analytical time.
There is a need for label-free SERS methods for detecting bacteria.